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Beginning Data Analysis with Python And Jupyter [Book] : Use powerful industry-standard tools to unlock new, actionable insight from your existing data.

Author: Alex Galea
Publisher: Birmingham : Packt Publishing Ltd, 2018.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Galea, Alex.
Beginning Data Analysis with Python And Jupyter [Book] : Use powerful industry-standard tools to unlock new, actionable insight from your existing data.
Birmingham : Packt Publishing Ltd, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Alex Galea
ISBN: 9781789534658 1789534658 9781789532029 1789532027
OCLC Number: 1041020992
Description: 1 online resource (194 pages)
Contents: 9781789532029-cov-High; 978-1-78953-202-9_ebook; Preface; Jupyter Fundamentals; Basic Functionality and Features; Subtopic A: What is a Jupyter Notebook and Why is it Useful?; Subtopic B: Navigating the Platform; Introducing Jupyter Notebooks; Subtopic C: Jupyter Features; Explore some of Jupyter's most useful features; Converting a Jupyter Notebook to a Python Script; Subtopic D: Python Libraries; Import the external libraries and set up the plotting environment; Our First Analysis --
The Boston Housing Dataset; Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame. Load the Boston housing datasetSubtopic B: Data Exploration; Explore the Boston housing dataset; Subtopic C: Introduction to Predictive Analytics with Jupyter Notebooks; Linear models with Seaborn and scikit-learn; Activity B: Building a Third-Order Polynomial Model; Subtopic D: Using Categorical Features for Segmentation Analysis; Create categorical fields from continuous variables and make segmented visualizations; Summary; Data Cleaning and Advanced Machine Learning; Preparing to Train a Predictive Model; Subtopic A: Determining a Plan for Predictive Analytics. Subtopic B: Preprocessing Data for Machine LearningExplore data preprocessing tools and methods; Activity A: Preparing to Train a Predictive Model for the Employee-Retention Problem; Training Classification Models; Subtopic A: Introduction to Classification Algorithms; Training two-feature classification models with scikit-learn; The plot_decision_regions Function; Training k-nearest neighbors for our model; Training a Random Forest; Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves; Using k-fold cross validation and validation curves in Python with scikit-learn. Subtopic C: Dimensionality Reduction TechniquesTraining a predictive model for the employee retention problem; Summary; Web Scraping and Interactive Visualizations; Scraping Web Page Data; Subtopic A: Introduction to HTTP Requests; Subtopic B: Making HTTP Requests in the Jupyter Notebook; Handling HTTP requests with Python in a Jupyter Notebook; Subtopic C: Parsing HTML in the Jupyter Notebook; Parsing HTML with Python in a Jupyter Notebook; Activity A: Web Scraping with Jupyter Notebooks; Interactive Visualizations; Subtopic A: Building a DataFrame to Store and Organize Data. Building and merging Pandas DataFramesSubtopic B: Introduction to Bokeh; Introduction to interactive visualizations with Bokeh; Activity B: Exploring Data with Interactive Visualizations; Summary; Index.

Abstract:

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the  Read more...

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